text.sliding_window

Builds a sliding window for data with a specified width.

text.sliding_window(
    data, width, axis=-1, name=None
)

Returns a tensor constructed from data, where each element in dimension axis is a slice of data starting at the corresponding position, with the given width and step size. I.e.:

  • result.shape.ndims = data.shape.ndims + 1
  • result[i1..iaxis, a] = data[i1..iaxis, a:a+width] (where 0 <= a < data[i1...iaxis].shape[0] - (width - 1)).

Note that each result row (along dimension axis) has width - 1 fewer items than the corresponding data row. If a data row has fewer than width items, then the corresponding result row will be empty. If you wish for the result rows to be the same size as the data rows, you can use pad_along_dimension to add width - 1 padding elements before calling this op.

Examples:

Sliding window (width=3) across a sequence of tokens:

# input: <string>[sequence_length]
input = tf.constant(["one", "two", "three", "four", "five", "six"])
# output: <string>[sequence_length-2, 3]
sliding_window(data=input, width=3, axis=0)
<tf.Tensor: shape=(4, 3), dtype=string, numpy=
    array([[b'one', b'two', b'three'],
           [b'two', b'three', b'four'],
           [b'three', b'four', b'five'],
           [b'four', b'five', b'six']], dtype=object)>

Sliding window (width=2) across the inner dimension of a ragged matrix containing a batch of token sequences:

# input: <string>[num_sentences, (num_words)]
input = tf.ragged.constant(
    [['Up', 'high', 'in', 'the', 'air'],
     ['Down', 'under', 'water'],
     ['Away', 'to', 'outer', 'space']])
# output: <string>[num_sentences, (num_word-1), 2]
sliding_window(input, width=2, axis=-1)
<tf.RaggedTensor [[[b'Up', b'high'], [b'high', b'in'], [b'in', b'the'],
                   [b'the', b'air']], [[b'Down', b'under'],
                   [b'under', b'water']],
                  [[b'Away', b'to'], [b'to', b'outer'],
                   [b'outer', b'space']]]>

Sliding window across the second dimension of a 3-D tensor containing batches of sequences of embedding vectors:

# input: <int32>[num_sequences, sequence_length, embedding_size]
input = tf.constant([
    [[1, 1, 1], [2, 2, 1], [3, 3, 1], [4, 4, 1], [5, 5, 1]],
    [[1, 1, 2], [2, 2, 2], [3, 3, 2], [4, 4, 2], [5, 5, 2]]])
# output: <int32>[num_sequences, sequence_length-1, 2, embedding_size]
sliding_window(data=input, width=2, axis=1)
<tf.Tensor: shape=(2, 4, 2, 3), dtype=int32, numpy=
    array([[[[1, 1, 1],
             [2, 2, 1]],
            [[2, 2, 1],
             [3, 3, 1]],
            [[3, 3, 1],
             [4, 4, 1]],
            [[4, 4, 1],
             [5, 5, 1]]],
           [[[1, 1, 2],
             [2, 2, 2]],
            [[2, 2, 2],
             [3, 3, 2]],
            [[3, 3, 2],
             [4, 4, 2]],
            [[4, 4, 2],
             [5, 5, 2]]]], dtype=int32)>

data <dtype> [O1...ON, A, I1...IM] A potentially ragged K-dimensional tensor with outer dimensions of size O1...ON; axis dimension of size A; and inner dimensions of size I1...IM. I.e. K = N + 1 + M, where N>=0 and M>=0.
width An integer constant specifying the width of the window. Must be greater than zero.
axis An integer constant specifying the axis along which sliding window is computed. Negative axis values from -K to -1 are supported.
name The name for this op (optional).

A K+1 dimensional tensor with the same dtype as data, where:

  • result[i1..iaxis, a] = data[i1..iaxis, a:a+width]
  • result.shape[:axis] = data.shape[:axis]
  • result.shape[axis] = data.shape[axis] - (width - 1)
  • result.shape[axis + 1] = width
  • result.shape[axis + 2:] = data.shape[axis + 1:]